{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import packages for question 1 and 2\n",
    "from bs4 import BeautifulSoup\n",
    "import requests\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load wiki data into df_ca\n",
    "url = requests.get('https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M').text\n",
    "df_ca = BeautifulSoup(url, 'lxml')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create an empty dataframe\n",
    "column_names = ['Postalcode','Borough','Neighborhood']\n",
    "toronto = pd.DataFrame(columns = column_names)\n",
    "\n",
    "# loop through table to find elements\n",
    "# tutorial:https://srome.github.io/Parsing-HTML-Tables-in-Python-with-BeautifulSoup-and-pandas/\n",
    "content = df_ca.find('div', class_='mw-parser-output')\n",
    "table = content.table.tbody\n",
    "postcode = 0\n",
    "borough = 0\n",
    "neighborhood = 0\n",
    "\n",
    "\n",
    "for tr in table.find_all('tr'):\n",
    "    i = 0\n",
    "    for td in tr.find_all('td'):\n",
    "        if i == 0:\n",
    "            postcode = td.text\n",
    "            i = i + 1\n",
    "        elif i == 1:\n",
    "            borough = td.text\n",
    "            i = i + 1\n",
    "        elif i == 2: \n",
    "            neighborhood = td.text.strip('\\n').replace(']','')\n",
    "    toronto = toronto.append({'Postalcode': postcode,'Borough': borough,'Neighborhood': neighborhood},ignore_index=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Postalcode</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighborhood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M1B</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Rouge, Malvern</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M1C</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Highland Creek, Rouge Hill, Port Union</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M1E</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Guildwood, Morningside, West Hill</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M1G</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Woburn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M1H</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Cedarbrae</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Postalcode      Borough                            Neighborhood\n",
       "0        M1B  Scarborough                          Rouge, Malvern\n",
       "1        M1C  Scarborough  Highland Creek, Rouge Hill, Port Union\n",
       "2        M1E  Scarborough       Guildwood, Morningside, West Hill\n",
       "3        M1G  Scarborough                                  Woburn\n",
       "4        M1H  Scarborough                               Cedarbrae"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#data cleaning\n",
    "toronto = toronto[toronto.Borough!='Not assigned']\n",
    "toronto = toronto[toronto.Borough!= 0]\n",
    "toronto.reset_index(drop = True, inplace = True)\n",
    "i = 0\n",
    "for i in range(0,toronto.shape[0]):\n",
    "    if toronto.iloc[i][2] == 'Not assigned':\n",
    "        toronto.iloc[i][2] = toronto.iloc[i][1]\n",
    "        i = i+1\n",
    "                                 \n",
    "df = toronto.groupby(['Postalcode','Borough'])['Neighborhood'].apply(', '.join).reset_index()\n",
    "df = df.dropna()\n",
    "empty = 'Not assigned'\n",
    "df = df[(df.Postalcode != empty ) & (df.Borough != empty) & (df.Neighborhood != empty)]\n",
    "def neighborhood_list(grouped):    \n",
    "    return ', '.join(sorted(grouped['Neighborhood'].tolist()))\n",
    "                    \n",
    "grp = df.groupby(['Postalcode', 'Borough'])\n",
    "df2 = grp.apply(neighborhood_list).reset_index(name='Neighborhood')\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(103, 3)\n"
     ]
    }
   ],
   "source": [
    "print(df2.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 2:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M1B</td>\n",
       "      <td>43.806686</td>\n",
       "      <td>-79.194353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M1C</td>\n",
       "      <td>43.784535</td>\n",
       "      <td>-79.160497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M1E</td>\n",
       "      <td>43.763573</td>\n",
       "      <td>-79.188711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M1G</td>\n",
       "      <td>43.770992</td>\n",
       "      <td>-79.216917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M1H</td>\n",
       "      <td>43.773136</td>\n",
       "      <td>-79.239476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M1J</td>\n",
       "      <td>43.744734</td>\n",
       "      <td>-79.239476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>M1K</td>\n",
       "      <td>43.727929</td>\n",
       "      <td>-79.262029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>M1L</td>\n",
       "      <td>43.711112</td>\n",
       "      <td>-79.284577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>M1M</td>\n",
       "      <td>43.716316</td>\n",
       "      <td>-79.239476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>M1N</td>\n",
       "      <td>43.692657</td>\n",
       "      <td>-79.264848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>M1P</td>\n",
       "      <td>43.757410</td>\n",
       "      <td>-79.273304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>M1R</td>\n",
       "      <td>43.750072</td>\n",
       "      <td>-79.295849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>M1S</td>\n",
       "      <td>43.794200</td>\n",
       "      <td>-79.262029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>M1T</td>\n",
       "      <td>43.781638</td>\n",
       "      <td>-79.304302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>M1V</td>\n",
       "      <td>43.815252</td>\n",
       "      <td>-79.284577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>M1W</td>\n",
       "      <td>43.799525</td>\n",
       "      <td>-79.318389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>M1X</td>\n",
       "      <td>43.836125</td>\n",
       "      <td>-79.205636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>M2H</td>\n",
       "      <td>43.803762</td>\n",
       "      <td>-79.363452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>M2J</td>\n",
       "      <td>43.778517</td>\n",
       "      <td>-79.346556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>M2K</td>\n",
       "      <td>43.786947</td>\n",
       "      <td>-79.385975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>M2L</td>\n",
       "      <td>43.757490</td>\n",
       "      <td>-79.374714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>M2M</td>\n",
       "      <td>43.789053</td>\n",
       "      <td>-79.408493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>M2N</td>\n",
       "      <td>43.770120</td>\n",
       "      <td>-79.408493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>M2P</td>\n",
       "      <td>43.752758</td>\n",
       "      <td>-79.400049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>M2R</td>\n",
       "      <td>43.782736</td>\n",
       "      <td>-79.442259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>M3A</td>\n",
       "      <td>43.753259</td>\n",
       "      <td>-79.329656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>M3B</td>\n",
       "      <td>43.745906</td>\n",
       "      <td>-79.352188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>M3C</td>\n",
       "      <td>43.725900</td>\n",
       "      <td>-79.340923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>M3H</td>\n",
       "      <td>43.754328</td>\n",
       "      <td>-79.442259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>M3J</td>\n",
       "      <td>43.767980</td>\n",
       "      <td>-79.487262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>M6C</td>\n",
       "      <td>43.693781</td>\n",
       "      <td>-79.428191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>M6E</td>\n",
       "      <td>43.689026</td>\n",
       "      <td>-79.453512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>M6G</td>\n",
       "      <td>43.669542</td>\n",
       "      <td>-79.422564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>M6H</td>\n",
       "      <td>43.669005</td>\n",
       "      <td>-79.442259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>M6J</td>\n",
       "      <td>43.647927</td>\n",
       "      <td>-79.419750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>M6K</td>\n",
       "      <td>43.636847</td>\n",
       "      <td>-79.428191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>M6L</td>\n",
       "      <td>43.713756</td>\n",
       "      <td>-79.490074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>M6M</td>\n",
       "      <td>43.691116</td>\n",
       "      <td>-79.476013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>M6N</td>\n",
       "      <td>43.673185</td>\n",
       "      <td>-79.487262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>M6P</td>\n",
       "      <td>43.661608</td>\n",
       "      <td>-79.464763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>M6R</td>\n",
       "      <td>43.648960</td>\n",
       "      <td>-79.456325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>M6S</td>\n",
       "      <td>43.651571</td>\n",
       "      <td>-79.484450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>M7A</td>\n",
       "      <td>43.662301</td>\n",
       "      <td>-79.389494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>M7R</td>\n",
       "      <td>43.636966</td>\n",
       "      <td>-79.615819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>M7Y</td>\n",
       "      <td>43.662744</td>\n",
       "      <td>-79.321558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>M8V</td>\n",
       "      <td>43.605647</td>\n",
       "      <td>-79.501321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>M8W</td>\n",
       "      <td>43.602414</td>\n",
       "      <td>-79.543484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>M8X</td>\n",
       "      <td>43.653654</td>\n",
       "      <td>-79.506944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>M8Y</td>\n",
       "      <td>43.636258</td>\n",
       "      <td>-79.498509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>M8Z</td>\n",
       "      <td>43.628841</td>\n",
       "      <td>-79.520999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>M9A</td>\n",
       "      <td>43.667856</td>\n",
       "      <td>-79.532242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>M9B</td>\n",
       "      <td>43.650943</td>\n",
       "      <td>-79.554724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>M9C</td>\n",
       "      <td>43.643515</td>\n",
       "      <td>-79.577201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>M9L</td>\n",
       "      <td>43.756303</td>\n",
       "      <td>-79.565963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>M9M</td>\n",
       "      <td>43.724766</td>\n",
       "      <td>-79.532242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>M9N</td>\n",
       "      <td>43.706876</td>\n",
       "      <td>-79.518188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>M9P</td>\n",
       "      <td>43.696319</td>\n",
       "      <td>-79.532242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>M9R</td>\n",
       "      <td>43.688905</td>\n",
       "      <td>-79.554724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>M9V</td>\n",
       "      <td>43.739416</td>\n",
       "      <td>-79.588437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>M9W</td>\n",
       "      <td>43.706748</td>\n",
       "      <td>-79.594054</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Postal Code   Latitude  Longitude\n",
       "0           M1B  43.806686 -79.194353\n",
       "1           M1C  43.784535 -79.160497\n",
       "2           M1E  43.763573 -79.188711\n",
       "3           M1G  43.770992 -79.216917\n",
       "4           M1H  43.773136 -79.239476\n",
       "5           M1J  43.744734 -79.239476\n",
       "6           M1K  43.727929 -79.262029\n",
       "7           M1L  43.711112 -79.284577\n",
       "8           M1M  43.716316 -79.239476\n",
       "9           M1N  43.692657 -79.264848\n",
       "10          M1P  43.757410 -79.273304\n",
       "11          M1R  43.750072 -79.295849\n",
       "12          M1S  43.794200 -79.262029\n",
       "13          M1T  43.781638 -79.304302\n",
       "14          M1V  43.815252 -79.284577\n",
       "15          M1W  43.799525 -79.318389\n",
       "16          M1X  43.836125 -79.205636\n",
       "17          M2H  43.803762 -79.363452\n",
       "18          M2J  43.778517 -79.346556\n",
       "19          M2K  43.786947 -79.385975\n",
       "20          M2L  43.757490 -79.374714\n",
       "21          M2M  43.789053 -79.408493\n",
       "22          M2N  43.770120 -79.408493\n",
       "23          M2P  43.752758 -79.400049\n",
       "24          M2R  43.782736 -79.442259\n",
       "25          M3A  43.753259 -79.329656\n",
       "26          M3B  43.745906 -79.352188\n",
       "27          M3C  43.725900 -79.340923\n",
       "28          M3H  43.754328 -79.442259\n",
       "29          M3J  43.767980 -79.487262\n",
       "..          ...        ...        ...\n",
       "73          M6C  43.693781 -79.428191\n",
       "74          M6E  43.689026 -79.453512\n",
       "75          M6G  43.669542 -79.422564\n",
       "76          M6H  43.669005 -79.442259\n",
       "77          M6J  43.647927 -79.419750\n",
       "78          M6K  43.636847 -79.428191\n",
       "79          M6L  43.713756 -79.490074\n",
       "80          M6M  43.691116 -79.476013\n",
       "81          M6N  43.673185 -79.487262\n",
       "82          M6P  43.661608 -79.464763\n",
       "83          M6R  43.648960 -79.456325\n",
       "84          M6S  43.651571 -79.484450\n",
       "85          M7A  43.662301 -79.389494\n",
       "86          M7R  43.636966 -79.615819\n",
       "87          M7Y  43.662744 -79.321558\n",
       "88          M8V  43.605647 -79.501321\n",
       "89          M8W  43.602414 -79.543484\n",
       "90          M8X  43.653654 -79.506944\n",
       "91          M8Y  43.636258 -79.498509\n",
       "92          M8Z  43.628841 -79.520999\n",
       "93          M9A  43.667856 -79.532242\n",
       "94          M9B  43.650943 -79.554724\n",
       "95          M9C  43.643515 -79.577201\n",
       "96          M9L  43.756303 -79.565963\n",
       "97          M9M  43.724766 -79.532242\n",
       "98          M9N  43.706876 -79.518188\n",
       "99          M9P  43.696319 -79.532242\n",
       "100         M9R  43.688905 -79.554724\n",
       "101         M9V  43.739416 -79.588437\n",
       "102         M9W  43.706748 -79.594054\n",
       "\n",
       "[103 rows x 3 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfll= pd.read_csv(\"http://cocl.us/Geospatial_data\")\n",
    "dfll"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Postalcode</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M1B</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Rouge, Malvern</td>\n",
       "      <td>43.806686</td>\n",
       "      <td>-79.194353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M1C</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Highland Creek, Rouge Hill, Port Union</td>\n",
       "      <td>43.784535</td>\n",
       "      <td>-79.160497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M1E</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Guildwood, Morningside, West Hill</td>\n",
       "      <td>43.763573</td>\n",
       "      <td>-79.188711</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M1G</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Woburn</td>\n",
       "      <td>43.770992</td>\n",
       "      <td>-79.216917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M1H</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Cedarbrae</td>\n",
       "      <td>43.773136</td>\n",
       "      <td>-79.239476</td>\n",
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       "  Postalcode      Borough                            Neighborhood   Latitude  \\\n",
       "0        M1B  Scarborough                          Rouge, Malvern  43.806686   \n",
       "1        M1C  Scarborough  Highland Creek, Rouge Hill, Port Union  43.784535   \n",
       "2        M1E  Scarborough       Guildwood, Morningside, West Hill  43.763573   \n",
       "3        M1G  Scarborough                                  Woburn  43.770992   \n",
       "4        M1H  Scarborough                               Cedarbrae  43.773136   \n",
       "\n",
       "   Longitude  \n",
       "0 -79.194353  \n",
       "1 -79.160497  \n",
       "2 -79.188711  \n",
       "3 -79.216917  \n",
       "4 -79.239476  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfll.rename(columns={'Postal Code':'Postalcode'}, inplace=True)\n",
    "dfll.set_index(\"Postalcode\")\n",
    "df2.set_index(\"Postalcode\")\n",
    "toronto_data=pd.merge(df2, dfll)\n",
    "toronto_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata (repodata.json): done\n",
      "Solving environment: - \n",
      "The environment is inconsistent, please check the package plan carefully\n",
      "The following packages are causing the inconsistency:\n",
      "\n",
      "  - defaults/osx-64::numba==0.44.1=py37h6440ff4_0\n",
      "  - defaults/osx-64::_anaconda_depends==2019.03=py37_0\n",
      "  - defaults/osx-64::anaconda==custom=py37_1\n",
      "done\n",
      "\n",
      "\n",
      "==> WARNING: A newer version of conda exists. <==\n",
      "  current version: 4.7.12\n",
      "  latest version: 4.8.0\n",
      "\n",
      "Please update conda by running\n",
      "\n",
      "    $ conda update -n base -c defaults conda\n",
      "\n",
      "\n",
      "\n",
      "## Package Plan ##\n",
      "\n",
      "  environment location: /anaconda3\n",
      "\n",
      "  added / updated specs:\n",
      "    - geopy\n",
      "\n",
      "\n",
      "The following packages will be downloaded:\n",
      "\n",
      "    package                    |            build\n",
      "    ---------------------------|-----------------\n",
      "    ca-certificates-2019.11.28 |       hecc5488_0         145 KB  conda-forge\n",
      "    certifi-2019.11.28         |           py37_0         148 KB  conda-forge\n",
      "    conda-4.8.0                |           py37_1         3.0 MB  conda-forge\n",
      "    geographiclib-1.50         |             py_0          34 KB  conda-forge\n",
      "    geopy-1.20.0               |             py_0          57 KB  conda-forge\n",
      "    openssl-1.1.1d             |       h0b31af3_0         1.9 MB  conda-forge\n",
      "    tbb-2019.8                 |       h770b8ee_0         157 KB  conda-forge\n",
      "    ------------------------------------------------------------\n",
      "                                           Total:         5.4 MB\n",
      "\n",
      "The following NEW packages will be INSTALLED:\n",
      "\n",
      "  geographiclib      conda-forge/noarch::geographiclib-1.50-py_0\n",
      "  geopy              conda-forge/noarch::geopy-1.20.0-py_0\n",
      "  tbb                conda-forge/osx-64::tbb-2019.8-h770b8ee_0\n",
      "\n",
      "The following packages will be UPDATED:\n",
      "\n",
      "  ca-certificates    pkgs/main::ca-certificates-2019.11.27~ --> conda-forge::ca-certificates-2019.11.28-hecc5488_0\n",
      "  conda                      pkgs/main::conda-4.7.12-py37_0 --> conda-forge::conda-4.8.0-py37_1\n",
      "\n",
      "The following packages will be SUPERSEDED by a higher-priority channel:\n",
      "\n",
      "  certifi                                         pkgs/main --> conda-forge\n",
      "  openssl              pkgs/main::openssl-1.1.1d-h1de35cc_3 --> conda-forge::openssl-1.1.1d-h0b31af3_0\n",
      "\n",
      "\n",
      "\n",
      "Downloading and Extracting Packages\n",
      "openssl-1.1.1d       | 1.9 MB    | ##################################### | 100% \n",
      "geographiclib-1.50   | 34 KB     | ##################################### | 100% \n",
      "conda-4.8.0          | 3.0 MB    | ##################################### | 100% \n",
      "certifi-2019.11.28   | 148 KB    | ##################################### | 100% \n",
      "ca-certificates-2019 | 145 KB    | ##################################### | 100% \n",
      "tbb-2019.8           | 157 KB    | ##################################### | 100% \n",
      "geopy-1.20.0         | 57 KB     | ##################################### | 100% \n",
      "Preparing transaction: done\n",
      "Verifying transaction: done\n",
      "Executing transaction: done\n",
      "Collecting package metadata (repodata.json): done\n",
      "Solving environment: done\n",
      "\n",
      "## Package Plan ##\n",
      "\n",
      "  environment location: /anaconda3\n",
      "\n",
      "  added / updated specs:\n",
      "    - folium=0.5.0\n",
      "\n",
      "\n",
      "The following packages will be downloaded:\n",
      "\n",
      "    package                    |            build\n",
      "    ---------------------------|-----------------\n",
      "    altair-4.0.0               |             py_0         606 KB  conda-forge\n",
      "    branca-0.3.1               |             py_0          25 KB  conda-forge\n",
      "    folium-0.5.0               |             py_0          45 KB  conda-forge\n",
      "    vincent-0.4.4              |             py_1          28 KB  conda-forge\n",
      "    ------------------------------------------------------------\n",
      "                                           Total:         704 KB\n",
      "\n",
      "The following NEW packages will be INSTALLED:\n",
      "\n",
      "  altair             conda-forge/noarch::altair-4.0.0-py_0\n",
      "  branca             conda-forge/noarch::branca-0.3.1-py_0\n",
      "  folium             conda-forge/noarch::folium-0.5.0-py_0\n",
      "  vincent            conda-forge/noarch::vincent-0.4.4-py_1\n",
      "\n",
      "\n",
      "\n",
      "Downloading and Extracting Packages\n",
      "altair-4.0.0         | 606 KB    | ##################################### | 100% \n",
      "branca-0.3.1         | 25 KB     | ##################################### | 100% \n",
      "folium-0.5.0         | 45 KB     | ##################################### | 100% \n",
      "vincent-0.4.4        | 28 KB     | ##################################### | 100% \n",
      "Preparing transaction: done\n",
      "Verifying transaction: done\n",
      "Executing transaction: done\n",
      "Libraries imported.\n"
     ]
    }
   ],
   "source": [
    "#import packages\n",
    "import json # library to handle JSON files\n",
    "!conda install -c conda-forge geopy --yes # uncomment this line if you haven't completed the Foursquare API lab\n",
    "from geopy.geocoders import Nominatim # convert an address into latitude and longitude values\n",
    "import requests # library to handle requests\n",
    "from pandas.io.json import json_normalize # tranform JSON file into a pandas dataframe\n",
    "# Matplotlib and associated plotting modules\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.colors as colors\n",
    "# import k-means from clustering stage\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.datasets.samples_generator import make_blobs\n",
    "!conda install -c conda-forge folium=0.5.0 --yes # uncomment this line if you haven't completed the Foursquare API lab\n",
    "import folium # map rendering library\n",
    "from bs4 import BeautifulSoup\n",
    "import lxml\n",
    "print('Libraries imported.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The geograpical coordinate of Toronto, ON, Canada are 43.653963, -79.387207.\n"
     ]
    }
   ],
   "source": [
    "#Use geopy library to get the latitude and longitude values of Toronto\n",
    "address = 'Toronto, ON, Canada'\n",
    "geolocator = Nominatim(user_agent=\"to_explorer\")\n",
    "location = geolocator.geocode(address)\n",
    "latitude = location.latitude\n",
    "longitude = location.longitude\n",
    "print('The geograpical coordinate of Toronto, ON, Canada are {}, {}.'.format(latitude, longitude))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
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</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x1a1ae0b4a8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create map of Toronto\n",
    "map_toronto = folium.Map(location=[latitude, longitude], zoom_start=10)\n",
    "\n",
    "# add markers to map\n",
    "for lat, lng, borough, neighborhood in zip(toronto_data['Latitude'], toronto_data['Longitude'], toronto_data['Borough'], toronto_data['Neighborhood']):\n",
    "    label = '{}, {}'.format(neighborhood, borough)\n",
    "    label = folium.Popup(label, parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lng],\n",
    "        radius=5,\n",
    "        popup=label,\n",
    "        color='blue',\n",
    "        fill=True,\n",
    "        fill_color='#3186cc',\n",
    "        fill_opacity=0.7,\n",
    "        parse_html=False).add_to(map_toronto)  \n",
    "    \n",
    "map_toronto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Scarborough', 'North York', 'East York', 'East Toronto',\n",
       "       'Central Toronto', 'Downtown Toronto', 'York', 'West Toronto',\n",
       "       \"Queen's Park\", 'Mississauga', 'Etobicoke'], dtype=object)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get unique Borough\n",
    "toronto_data['Borough'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Postalcode</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M4E</td>\n",
       "      <td>East Toronto</td>\n",
       "      <td>The Beaches</td>\n",
       "      <td>43.6764</td>\n",
       "      <td>-79.293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M4K</td>\n",
       "      <td>East Toronto</td>\n",
       "      <td>The Danforth West, Riverdale</td>\n",
       "      <td>43.6796</td>\n",
       "      <td>-79.3522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M4L</td>\n",
       "      <td>East Toronto</td>\n",
       "      <td>The Beaches West, India Bazaar</td>\n",
       "      <td>43.669</td>\n",
       "      <td>-79.3156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M4M</td>\n",
       "      <td>East Toronto</td>\n",
       "      <td>Studio District</td>\n",
       "      <td>43.6595</td>\n",
       "      <td>-79.3409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M7Y</td>\n",
       "      <td>East Toronto</td>\n",
       "      <td>Business Reply Mail Processing Centre 969 Eastern</td>\n",
       "      <td>43.6627</td>\n",
       "      <td>-79.3216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M4N</td>\n",
       "      <td>Central Toronto</td>\n",
       "      <td>Lawrence Park</td>\n",
       "      <td>43.728</td>\n",
       "      <td>-79.3888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>M4P</td>\n",
       "      <td>Central Toronto</td>\n",
       "      <td>Davisville North</td>\n",
       "      <td>43.7128</td>\n",
       "      <td>-79.3902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>M4R</td>\n",
       "      <td>Central Toronto</td>\n",
       "      <td>North Toronto West</td>\n",
       "      <td>43.7154</td>\n",
       "      <td>-79.4057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>M4S</td>\n",
       "      <td>Central Toronto</td>\n",
       "      <td>Davisville</td>\n",
       "      <td>43.7043</td>\n",
       "      <td>-79.3888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>M4T</td>\n",
       "      <td>Central Toronto</td>\n",
       "      <td>Moore Park, Summerhill East</td>\n",
       "      <td>43.6896</td>\n",
       "      <td>-79.3832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>M4V</td>\n",
       "      <td>Central Toronto</td>\n",
       "      <td>Deer Park, Forest Hill SE, Rathnelly, South Hi...</td>\n",
       "      <td>43.6864</td>\n",
       "      <td>-79.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>M5N</td>\n",
       "      <td>Central Toronto</td>\n",
       "      <td>Roselawn</td>\n",
       "      <td>43.7117</td>\n",
       "      <td>-79.4169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>M5P</td>\n",
       "      <td>Central Toronto</td>\n",
       "      <td>Forest Hill North, Forest Hill West</td>\n",
       "      <td>43.6969</td>\n",
       "      <td>-79.4113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>M5R</td>\n",
       "      <td>Central Toronto</td>\n",
       "      <td>The Annex, North Midtown, Yorkville</td>\n",
       "      <td>43.6727</td>\n",
       "      <td>-79.4057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>M4W</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Rosedale</td>\n",
       "      <td>43.6796</td>\n",
       "      <td>-79.3775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>M4X</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Cabbagetown, St. James Town</td>\n",
       "      <td>43.668</td>\n",
       "      <td>-79.3677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>M4Y</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Church and Wellesley</td>\n",
       "      <td>43.6659</td>\n",
       "      <td>-79.3832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>M5A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Harbourfront</td>\n",
       "      <td>43.6543</td>\n",
       "      <td>-79.3606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>M5B</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Ryerson, Garden District</td>\n",
       "      <td>43.6572</td>\n",
       "      <td>-79.3789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>M5C</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>St. James Town</td>\n",
       "      <td>43.6515</td>\n",
       "      <td>-79.3754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>M5E</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Berczy Park</td>\n",
       "      <td>43.6448</td>\n",
       "      <td>-79.3733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>M5G</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Central Bay Street</td>\n",
       "      <td>43.658</td>\n",
       "      <td>-79.3874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>M5H</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Adelaide, King, Richmond</td>\n",
       "      <td>43.6506</td>\n",
       "      <td>-79.3846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>M5J</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Harbourfront East, Toronto Islands, Union Station</td>\n",
       "      <td>43.6408</td>\n",
       "      <td>-79.3818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>M5K</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Design Exchange, Toronto Dominion Centre</td>\n",
       "      <td>43.6472</td>\n",
       "      <td>-79.3816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>M5L</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Commerce Court, Victoria Hotel</td>\n",
       "      <td>43.6482</td>\n",
       "      <td>-79.3798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>M5S</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Harbord, University of Toronto</td>\n",
       "      <td>43.6627</td>\n",
       "      <td>-79.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>M5T</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Chinatown, Grange Park, Kensington Market</td>\n",
       "      <td>43.6532</td>\n",
       "      <td>-79.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>M5V</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>CN Tower, Bathurst Quay, Island airport, Harbo...</td>\n",
       "      <td>43.6289</td>\n",
       "      <td>-79.3944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>M5W</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Stn A PO Boxes 25 The Esplanade</td>\n",
       "      <td>43.6464</td>\n",
       "      <td>-79.3748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>M5X</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>First Canadian Place, Underground city</td>\n",
       "      <td>43.6484</td>\n",
       "      <td>-79.3823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>M6G</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Christie</td>\n",
       "      <td>43.6695</td>\n",
       "      <td>-79.4226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>M9A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Queen's Park</td>\n",
       "      <td>43.6679</td>\n",
       "      <td>-79.5322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>M6H</td>\n",
       "      <td>West Toronto</td>\n",
       "      <td>Dovercourt Village, Dufferin</td>\n",
       "      <td>43.669</td>\n",
       "      <td>-79.4423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>M6J</td>\n",
       "      <td>West Toronto</td>\n",
       "      <td>Little Portugal, Trinity</td>\n",
       "      <td>43.6479</td>\n",
       "      <td>-79.4197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>M6K</td>\n",
       "      <td>West Toronto</td>\n",
       "      <td>Brockton, Exhibition Place, Parkdale Village</td>\n",
       "      <td>43.6368</td>\n",
       "      <td>-79.4282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>M6P</td>\n",
       "      <td>West Toronto</td>\n",
       "      <td>High Park, The Junction South</td>\n",
       "      <td>43.6616</td>\n",
       "      <td>-79.4648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>M6R</td>\n",
       "      <td>West Toronto</td>\n",
       "      <td>Parkdale, Roncesvalles</td>\n",
       "      <td>43.649</td>\n",
       "      <td>-79.4563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>M6S</td>\n",
       "      <td>West Toronto</td>\n",
       "      <td>Runnymede, Swansea</td>\n",
       "      <td>43.6516</td>\n",
       "      <td>-79.4844</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Postalcode           Borough  \\\n",
       "0         M4E      East Toronto   \n",
       "1         M4K      East Toronto   \n",
       "2         M4L      East Toronto   \n",
       "3         M4M      East Toronto   \n",
       "4         M7Y      East Toronto   \n",
       "5         M4N   Central Toronto   \n",
       "6         M4P   Central Toronto   \n",
       "7         M4R   Central Toronto   \n",
       "8         M4S   Central Toronto   \n",
       "9         M4T   Central Toronto   \n",
       "10        M4V   Central Toronto   \n",
       "11        M5N   Central Toronto   \n",
       "12        M5P   Central Toronto   \n",
       "13        M5R   Central Toronto   \n",
       "14        M4W  Downtown Toronto   \n",
       "15        M4X  Downtown Toronto   \n",
       "16        M4Y  Downtown Toronto   \n",
       "17        M5A  Downtown Toronto   \n",
       "18        M5B  Downtown Toronto   \n",
       "19        M5C  Downtown Toronto   \n",
       "20        M5E  Downtown Toronto   \n",
       "21        M5G  Downtown Toronto   \n",
       "22        M5H  Downtown Toronto   \n",
       "23        M5J  Downtown Toronto   \n",
       "24        M5K  Downtown Toronto   \n",
       "25        M5L  Downtown Toronto   \n",
       "26        M5S  Downtown Toronto   \n",
       "27        M5T  Downtown Toronto   \n",
       "28        M5V  Downtown Toronto   \n",
       "29        M5W  Downtown Toronto   \n",
       "30        M5X  Downtown Toronto   \n",
       "31        M6G  Downtown Toronto   \n",
       "32        M9A  Downtown Toronto   \n",
       "33        M6H      West Toronto   \n",
       "34        M6J      West Toronto   \n",
       "35        M6K      West Toronto   \n",
       "36        M6P      West Toronto   \n",
       "37        M6R      West Toronto   \n",
       "38        M6S      West Toronto   \n",
       "\n",
       "                                         Neighborhood Latitude Longitude  \n",
       "0                                         The Beaches  43.6764   -79.293  \n",
       "1                        The Danforth West, Riverdale  43.6796  -79.3522  \n",
       "2                      The Beaches West, India Bazaar   43.669  -79.3156  \n",
       "3                                     Studio District  43.6595  -79.3409  \n",
       "4   Business Reply Mail Processing Centre 969 Eastern  43.6627  -79.3216  \n",
       "5                                       Lawrence Park   43.728  -79.3888  \n",
       "6                                    Davisville North  43.7128  -79.3902  \n",
       "7                                  North Toronto West  43.7154  -79.4057  \n",
       "8                                          Davisville  43.7043  -79.3888  \n",
       "9                         Moore Park, Summerhill East  43.6896  -79.3832  \n",
       "10  Deer Park, Forest Hill SE, Rathnelly, South Hi...  43.6864     -79.4  \n",
       "11                                           Roselawn  43.7117  -79.4169  \n",
       "12                Forest Hill North, Forest Hill West  43.6969  -79.4113  \n",
       "13                The Annex, North Midtown, Yorkville  43.6727  -79.4057  \n",
       "14                                           Rosedale  43.6796  -79.3775  \n",
       "15                        Cabbagetown, St. James Town   43.668  -79.3677  \n",
       "16                               Church and Wellesley  43.6659  -79.3832  \n",
       "17                                       Harbourfront  43.6543  -79.3606  \n",
       "18                           Ryerson, Garden District  43.6572  -79.3789  \n",
       "19                                     St. James Town  43.6515  -79.3754  \n",
       "20                                        Berczy Park  43.6448  -79.3733  \n",
       "21                                 Central Bay Street   43.658  -79.3874  \n",
       "22                           Adelaide, King, Richmond  43.6506  -79.3846  \n",
       "23  Harbourfront East, Toronto Islands, Union Station  43.6408  -79.3818  \n",
       "24           Design Exchange, Toronto Dominion Centre  43.6472  -79.3816  \n",
       "25                     Commerce Court, Victoria Hotel  43.6482  -79.3798  \n",
       "26                     Harbord, University of Toronto  43.6627     -79.4  \n",
       "27          Chinatown, Grange Park, Kensington Market  43.6532     -79.4  \n",
       "28  CN Tower, Bathurst Quay, Island airport, Harbo...  43.6289  -79.3944  \n",
       "29                    Stn A PO Boxes 25 The Esplanade  43.6464  -79.3748  \n",
       "30             First Canadian Place, Underground city  43.6484  -79.3823  \n",
       "31                                           Christie  43.6695  -79.4226  \n",
       "32                                       Queen's Park  43.6679  -79.5322  \n",
       "33                       Dovercourt Village, Dufferin   43.669  -79.4423  \n",
       "34                           Little Portugal, Trinity  43.6479  -79.4197  \n",
       "35       Brockton, Exhibition Place, Parkdale Village  43.6368  -79.4282  \n",
       "36                      High Park, The Junction South  43.6616  -79.4648  \n",
       "37                             Parkdale, Roncesvalles   43.649  -79.4563  \n",
       "38                                 Runnymede, Swansea  43.6516  -79.4844  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we only analyze the Borough contain the word Toronto\n",
    "import numpy as np\n",
    "toronto_dataE = toronto_data[toronto_data['Borough'] == 'East Toronto'].reset_index(drop=True)\n",
    "toronto_dataC = toronto_data[toronto_data['Borough'] == 'Central Toronto'].reset_index(drop=True)\n",
    "toronto_dataD = toronto_data[toronto_data['Borough'] == 'Downtown Toronto'].reset_index(drop=True)\n",
    "toronto_dataW = toronto_data[toronto_data['Borough'] == 'West Toronto'].reset_index(drop=True)\n",
    "toronto_data2 = np.vstack((toronto_dataE,toronto_dataC,toronto_dataD,toronto_dataW))\n",
    "toronto_data2 = pd.DataFrame(toronto_data2)\n",
    "toronto_data2.rename(columns={0:'Postalcode',1:'Borough',2:'Neighborhood',3:'Latitude',4:'Longitude'}, inplace=True)\n",
    "toronto_data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your credentails:\n",
      "CLIENT_ID: TD0N5N3O0P1FEQG1FNZE0PMXULS5RKOAKEVVBBS0PEEHZREA\n",
      "CLIENT_SECRET:YP5N1KZAPRGUONVFE3ZMMBASURZ1UP2AO02G1UNXJ1S15ZBI\n"
     ]
    }
   ],
   "source": [
    "CLIENT_ID = 'TD0N5N3O0P1FEQG1FNZE0PMXULS5RKOAKEVVBBS0PEEHZREA' # your Foursquare ID\n",
    "CLIENT_SECRET = 'YP5N1KZAPRGUONVFE3ZMMBASURZ1UP2AO02G1UNXJ1S15ZBI' # your Foursquare Secret\n",
    "VERSION = '20180605' # Foursquare API version\n",
    "\n",
    "print('Your credentails:')\n",
    "print('CLIENT_ID: ' + CLIENT_ID)\n",
    "print('CLIENT_SECRET:' + CLIENT_SECRET)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The Beaches'"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#examine the first neiborhood\n",
    "toronto_data2.loc[0, 'Neighborhood']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Latitude and longitude values of The Beaches are 43.67635739999999, -79.2930312.\n"
     ]
    }
   ],
   "source": [
    "#get its geo data\n",
    "neighborhood_latitude = toronto_data2.loc[0, 'Latitude'] # neighborhood latitude value\n",
    "neighborhood_longitude = toronto_data2.loc[0, 'Longitude'] # neighborhood longitude value\n",
    "\n",
    "neighborhood_name = toronto_data2.loc[0, 'Neighborhood'] # neighborhood name\n",
    "\n",
    "print('Latitude and longitude values of {} are {}, {}.'.format(neighborhood_name, \n",
    "                                                               neighborhood_latitude, \n",
    "                                                               neighborhood_longitude))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the top 100 venues in Rouge and Malvern\n",
    "LIMIT = 100 \n",
    "radius = 500 \n",
    "url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(\n",
    "    CLIENT_ID, \n",
    "    CLIENT_SECRET, \n",
    "    VERSION, \n",
    "    neighborhood_latitude, \n",
    "    neighborhood_longitude, \n",
    "    radius, \n",
    "    LIMIT)\n",
    "results = requests.get(url).json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function that extracts the category of the venue\n",
    "def get_category_type(row):\n",
    "    try:\n",
    "        categories_list = row['categories']\n",
    "    except:\n",
    "        categories_list = row['venue.categories']\n",
    "        \n",
    "    if len(categories_list) == 0:\n",
    "        return None\n",
    "    else:\n",
    "        return categories_list[0]['name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>categories</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Glen Manor Ravine</td>\n",
       "      <td>Trail</td>\n",
       "      <td>43.676821</td>\n",
       "      <td>-79.293942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Big Carrot Natural Food Market</td>\n",
       "      <td>Health Food Store</td>\n",
       "      <td>43.678879</td>\n",
       "      <td>-79.297734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Grover Pub and Grub</td>\n",
       "      <td>Pub</td>\n",
       "      <td>43.679181</td>\n",
       "      <td>-79.297215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Domino's Pizza</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>43.679058</td>\n",
       "      <td>-79.297382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Upper Beaches</td>\n",
       "      <td>Neighborhood</td>\n",
       "      <td>43.680563</td>\n",
       "      <td>-79.292869</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 name         categories        lat        lng\n",
       "0                   Glen Manor Ravine              Trail  43.676821 -79.293942\n",
       "1  The Big Carrot Natural Food Market  Health Food Store  43.678879 -79.297734\n",
       "2                 Grover Pub and Grub                Pub  43.679181 -79.297215\n",
       "3                      Domino's Pizza        Pizza Place  43.679058 -79.297382\n",
       "4                       Upper Beaches       Neighborhood  43.680563 -79.292869"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#\n",
    "venues = results['response']['groups'][0]['items']\n",
    "    \n",
    "nearby_venues = json_normalize(venues) # flatten JSON\n",
    "\n",
    "# filter columns\n",
    "filtered_columns = ['venue.name', 'venue.categories', 'venue.location.lat', 'venue.location.lng']\n",
    "nearby_venues =nearby_venues.loc[:, filtered_columns]\n",
    "\n",
    "# filter the category for each row\n",
    "nearby_venues['venue.categories'] = nearby_venues.apply(get_category_type, axis=1)\n",
    "\n",
    "# clean columns\n",
    "nearby_venues.columns = [col.split(\".\")[-1] for col in nearby_venues.columns]\n",
    "\n",
    "nearby_venues.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 venues were returned by Foursquare.\n"
     ]
    }
   ],
   "source": [
    "print('{} venues were returned by Foursquare.'.format(nearby_venues.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "#repeat the same process to all the neighborhoods in Toronto\n",
    "def getNearbyVenues(names, latitudes, longitudes, radius=500):\n",
    "    \n",
    "    venues_list=[]\n",
    "    for name, lat, lng in zip(names, latitudes, longitudes):\n",
    "        print(name)\n",
    "            \n",
    "        # create the API request URL\n",
    "        url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(\n",
    "            CLIENT_ID, \n",
    "            CLIENT_SECRET, \n",
    "            VERSION, \n",
    "            lat, \n",
    "            lng, \n",
    "            radius, \n",
    "            LIMIT)\n",
    "            \n",
    "        # make the GET request\n",
    "        results = requests.get(url).json()[\"response\"]['groups'][0]['items']\n",
    "        \n",
    "        # return only relevant information for each nearby venue\n",
    "        venues_list.append([(\n",
    "            name, \n",
    "            lat, \n",
    "            lng, \n",
    "            v['venue']['name'], \n",
    "            v['venue']['location']['lat'], \n",
    "            v['venue']['location']['lng'],  \n",
    "            v['venue']['categories'][0]['name']) for v in results])\n",
    "\n",
    "    nearby_venues = pd.DataFrame([item for venue_list in venues_list for item in venue_list])\n",
    "    nearby_venues.columns = ['Neighborhood', \n",
    "                  'Neighborhood Latitude', \n",
    "                  'Neighborhood Longitude', \n",
    "                  'Venue', \n",
    "                  'Venue Latitude', \n",
    "                  'Venue Longitude', \n",
    "                  'Venue Category']\n",
    "    \n",
    "    return(nearby_venues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Beaches\n",
      "The Danforth West, Riverdale\n",
      "The Beaches West, India Bazaar\n",
      "Studio District\n",
      "Business Reply Mail Processing Centre 969 Eastern\n",
      "Lawrence Park\n",
      "Davisville North\n",
      "North Toronto West\n",
      "Davisville\n",
      "Moore Park, Summerhill East\n",
      "Deer Park, Forest Hill SE, Rathnelly, South Hill, Summerhill West\n",
      "Roselawn\n",
      "Forest Hill North, Forest Hill West\n",
      "The Annex, North Midtown, Yorkville\n",
      "Rosedale\n",
      "Cabbagetown, St. James Town\n",
      "Church and Wellesley\n",
      "Harbourfront\n",
      "Ryerson, Garden District\n",
      "St. James Town\n",
      "Berczy Park\n",
      "Central Bay Street\n",
      "Adelaide, King, Richmond\n",
      "Harbourfront East, Toronto Islands, Union Station\n",
      "Design Exchange, Toronto Dominion Centre\n",
      "Commerce Court, Victoria Hotel\n",
      "Harbord, University of Toronto\n",
      "Chinatown, Grange Park, Kensington Market\n",
      "CN Tower, Bathurst Quay, Island airport, Harbourfront West, King and Spadina, Railway Lands, South Niagara\n",
      "Stn A PO Boxes 25 The Esplanade\n",
      "First Canadian Place, Underground city\n",
      "Christie\n",
      "Queen's Park\n",
      "Dovercourt Village, Dufferin\n",
      "Little Portugal, Trinity\n",
      "Brockton, Exhibition Place, Parkdale Village\n",
      "High Park, The Junction South\n",
      "Parkdale, Roncesvalles\n",
      "Runnymede, Swansea\n"
     ]
    }
   ],
   "source": [
    "#get the name of toronto venues\n",
    "toronto_venues = getNearbyVenues(names=toronto_data2['Neighborhood'],\n",
    "                                   latitudes=toronto_data2['Latitude'],\n",
    "                                   longitudes=toronto_data2['Longitude']\n",
    "                                  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                   Neighborhood Latitude  \\\n",
      "Neighborhood                                                               \n",
      "Adelaide, King, Richmond                                             100   \n",
      "Berczy Park                                                           56   \n",
      "Brockton, Exhibition Place, Parkdale Village                          24   \n",
      "Business Reply Mail Processing Centre 969 Eastern                     17   \n",
      "\n",
      "                                                   Neighborhood Longitude  \\\n",
      "Neighborhood                                                                \n",
      "Adelaide, King, Richmond                                              100   \n",
      "Berczy Park                                                            56   \n",
      "Brockton, Exhibition Place, Parkdale Village                           24   \n",
      "Business Reply Mail Processing Centre 969 Eastern                      17   \n",
      "\n",
      "                                                   Venue  Venue Latitude  \\\n",
      "Neighborhood                                                               \n",
      "Adelaide, King, Richmond                             100             100   \n",
      "Berczy Park                                           56              56   \n",
      "Brockton, Exhibition Place, Parkdale Village          24              24   \n",
      "Business Reply Mail Processing Centre 969 Eastern     17              17   \n",
      "\n",
      "                                                   Venue Longitude  \\\n",
      "Neighborhood                                                         \n",
      "Adelaide, King, Richmond                                       100   \n",
      "Berczy Park                                                     56   \n",
      "Brockton, Exhibition Place, Parkdale Village                    24   \n",
      "Business Reply Mail Processing Centre 969 Eastern               17   \n",
      "\n",
      "                                                   Venue Category  \n",
      "Neighborhood                                                       \n",
      "Adelaide, King, Richmond                                      100  \n",
      "Berczy Park                                                    56  \n",
      "Brockton, Exhibition Place, Parkdale Village                   24  \n",
      "Business Reply Mail Processing Centre 969 Eastern              17  \n"
     ]
    }
   ],
   "source": [
    "toronto_venues.head(3)\n",
    "print(toronto_venues.groupby('Neighborhood').count()[:4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# one hot encoding\n",
    "toronto_onehot = pd.get_dummies(toronto_venues[['Venue Category']], prefix=\"\", prefix_sep=\"\")\n",
    "\n",
    "# add neighborhood column back to dataframe\n",
    "toronto_onehot['Neighborhood'] = toronto_venues['Neighborhood'] \n",
    "\n",
    "# move neighborhood column to the first column\n",
    "fixed_columns = [toronto_onehot.columns[-1]] + list(toronto_onehot.columns[:-1])\n",
    "toronto_onehot = toronto_onehot[fixed_columns]\n",
    "\n",
    "toronto_grouped = toronto_onehot.groupby('Neighborhood').mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adelaide, King, Richmond</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Café</td>\n",
       "      <td>Steakhouse</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Salad Place</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Berczy Park</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Cocktail Bar</td>\n",
       "      <td>Café</td>\n",
       "      <td>Steakhouse</td>\n",
       "      <td>Farmers Market</td>\n",
       "      <td>Cheese Shop</td>\n",
       "      <td>Beer Bar</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Seafood Restaurant</td>\n",
       "      <td>Hotel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brockton, Exhibition Place, Parkdale Village</td>\n",
       "      <td>Café</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Gym</td>\n",
       "      <td>Pet Store</td>\n",
       "      <td>Performing Arts Venue</td>\n",
       "      <td>Music Venue</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Intersection</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Business Reply Mail Processing Centre 969 Eastern</td>\n",
       "      <td>Light Rail Station</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Brewery</td>\n",
       "      <td>Burrito Place</td>\n",
       "      <td>Auto Workshop</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Farmers Market</td>\n",
       "      <td>Spa</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Pizza Place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CN Tower, Bathurst Quay, Island airport, Harbo...</td>\n",
       "      <td>Airport Terminal</td>\n",
       "      <td>Airport Lounge</td>\n",
       "      <td>Boat or Ferry</td>\n",
       "      <td>Boutique</td>\n",
       "      <td>Rental Car Location</td>\n",
       "      <td>Plane</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Harbor / Marina</td>\n",
       "      <td>Sculpture Garden</td>\n",
       "      <td>Airport Service</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        Neighborhood 1st Most Common Venue  \\\n",
       "0                           Adelaide, King, Richmond           Coffee Shop   \n",
       "1                                        Berczy Park           Coffee Shop   \n",
       "2       Brockton, Exhibition Place, Parkdale Village                  Café   \n",
       "3  Business Reply Mail Processing Centre 969 Eastern    Light Rail Station   \n",
       "4  CN Tower, Bathurst Quay, Island airport, Harbo...      Airport Terminal   \n",
       "\n",
       "  2nd Most Common Venue 3rd Most Common Venue 4th Most Common Venue  \\\n",
       "0                  Café            Steakhouse                   Bar   \n",
       "1          Cocktail Bar                  Café            Steakhouse   \n",
       "2           Yoga Studio        Breakfast Spot           Coffee Shop   \n",
       "3           Yoga Studio               Brewery         Burrito Place   \n",
       "4        Airport Lounge         Boat or Ferry              Boutique   \n",
       "\n",
       "  5th Most Common Venue 6th Most Common Venue  7th Most Common Venue  \\\n",
       "0           Salad Place                Bakery           Burger Joint   \n",
       "1        Farmers Market           Cheese Shop               Beer Bar   \n",
       "2                   Gym             Pet Store  Performing Arts Venue   \n",
       "3         Auto Workshop  Fast Food Restaurant         Farmers Market   \n",
       "4   Rental Car Location                 Plane            Coffee Shop   \n",
       "\n",
       "  8th Most Common Venue 9th Most Common Venue 10th Most Common Venue  \n",
       "0            Restaurant       Thai Restaurant       Sushi Restaurant  \n",
       "1                Bakery    Seafood Restaurant                  Hotel  \n",
       "2           Music Venue    Italian Restaurant           Intersection  \n",
       "3                   Spa            Restaurant            Pizza Place  \n",
       "4       Harbor / Marina      Sculpture Garden        Airport Service  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define a function to get the most common venues\n",
    "def return_most_common_venues(row, num_top_venues):\n",
    "    row_categories = row.iloc[1:]\n",
    "    row_categories_sorted = row_categories.sort_values(ascending=False)\n",
    "    \n",
    "    return row_categories_sorted.index.values[0:num_top_venues]\n",
    "\n",
    "# get the 10 most common\n",
    "num_top_venues = 10\n",
    "\n",
    "indicators = ['st', 'nd', 'rd']\n",
    "\n",
    "# create columns according to number of top venues\n",
    "columns = ['Neighborhood']\n",
    "for ind in np.arange(num_top_venues):\n",
    "    try:\n",
    "        columns.append('{}{} Most Common Venue'.format(ind+1, indicators[ind]))\n",
    "    except:\n",
    "        columns.append('{}th Most Common Venue'.format(ind+1))\n",
    "\n",
    "# create a new dataframe\n",
    "neighborhoods_venues_sorted = pd.DataFrame(columns=columns)\n",
    "neighborhoods_venues_sorted['Neighborhood'] = toronto_grouped['Neighborhood']\n",
    "\n",
    "for ind in np.arange(toronto_grouped.shape[0]):\n",
    "    neighborhoods_venues_sorted.iloc[ind, 1:] = return_most_common_venues(toronto_grouped.iloc[ind, :], num_top_venues)\n",
    "neighborhoods_venues_sorted.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int32)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set number of clusters\n",
    "kclusters = 5\n",
    "\n",
    "toronto_grouped_clustering = toronto_grouped.drop('Neighborhood', 1)\n",
    "\n",
    "# run k-means clustering\n",
    "kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(toronto_grouped_clustering)\n",
    "\n",
    "# check cluster labels generated for each row in the dataframe\n",
    "kmeans.labels_[0:10]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Postalcode</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Cluster Labels</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M4E</td>\n",
       "      <td>East Toronto</td>\n",
       "      <td>The Beaches</td>\n",
       "      <td>43.6764</td>\n",
       "      <td>-79.293</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Health Food Store</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Pub</td>\n",
       "      <td>Trail</td>\n",
       "      <td>Discount Store</td>\n",
       "      <td>Department Store</td>\n",
       "      <td>Dessert Shop</td>\n",
       "      <td>Dim Sum Restaurant</td>\n",
       "      <td>Diner</td>\n",
       "      <td>Wings Joint</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M4K</td>\n",
       "      <td>East Toronto</td>\n",
       "      <td>The Danforth West, Riverdale</td>\n",
       "      <td>43.6796</td>\n",
       "      <td>-79.3522</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Greek Restaurant</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Ice Cream Shop</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Bookstore</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Pub</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Liquor Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M4L</td>\n",
       "      <td>East Toronto</td>\n",
       "      <td>The Beaches West, India Bazaar</td>\n",
       "      <td>43.669</td>\n",
       "      <td>-79.3156</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "      <td>Liquor Store</td>\n",
       "      <td>Fish &amp; Chips Shop</td>\n",
       "      <td>Pub</td>\n",
       "      <td>Ice Cream Shop</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Burrito Place</td>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Pizza Place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M4M</td>\n",
       "      <td>East Toronto</td>\n",
       "      <td>Studio District</td>\n",
       "      <td>43.6595</td>\n",
       "      <td>-79.3409</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Café</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Comfort Food Restaurant</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Brewery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M7Y</td>\n",
       "      <td>East Toronto</td>\n",
       "      <td>Business Reply Mail Processing Centre 969 Eastern</td>\n",
       "      <td>43.6627</td>\n",
       "      <td>-79.3216</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Light Rail Station</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Brewery</td>\n",
       "      <td>Burrito Place</td>\n",
       "      <td>Auto Workshop</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Farmers Market</td>\n",
       "      <td>Spa</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Pizza Place</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Postalcode       Borough                                       Neighborhood  \\\n",
       "0        M4E  East Toronto                                        The Beaches   \n",
       "1        M4K  East Toronto                       The Danforth West, Riverdale   \n",
       "2        M4L  East Toronto                     The Beaches West, India Bazaar   \n",
       "3        M4M  East Toronto                                    Studio District   \n",
       "4        M7Y  East Toronto  Business Reply Mail Processing Centre 969 Eastern   \n",
       "\n",
       "  Latitude Longitude  Cluster Labels 1st Most Common Venue  \\\n",
       "0  43.6764   -79.293             0.0     Health Food Store   \n",
       "1  43.6796  -79.3522             0.0      Greek Restaurant   \n",
       "2   43.669  -79.3156             0.0      Sushi Restaurant   \n",
       "3  43.6595  -79.3409             0.0                  Café   \n",
       "4  43.6627  -79.3216             0.0    Light Rail Station   \n",
       "\n",
       "  2nd Most Common Venue 3rd Most Common Venue 4th Most Common Venue  \\\n",
       "0           Pizza Place                   Pub                 Trail   \n",
       "1           Coffee Shop        Ice Cream Shop            Restaurant   \n",
       "2          Liquor Store     Fish & Chips Shop                   Pub   \n",
       "3           Coffee Shop             Gastropub                Bakery   \n",
       "4           Yoga Studio               Brewery         Burrito Place   \n",
       "\n",
       "    5th Most Common Venue 6th Most Common Venue 7th Most Common Venue  \\\n",
       "0          Discount Store      Department Store          Dessert Shop   \n",
       "1  Furniture / Home Store             Bookstore    Italian Restaurant   \n",
       "2          Ice Cream Shop  Fast Food Restaurant         Burrito Place   \n",
       "3      Italian Restaurant   American Restaurant           Yoga Studio   \n",
       "4           Auto Workshop  Fast Food Restaurant        Farmers Market   \n",
       "\n",
       "     8th Most Common Venue 9th Most Common Venue 10th Most Common Venue  \n",
       "0       Dim Sum Restaurant                 Diner            Wings Joint  \n",
       "1                      Pub           Pizza Place           Liquor Store  \n",
       "2             Burger Joint    Italian Restaurant            Pizza Place  \n",
       "3  Comfort Food Restaurant        Sandwich Place                Brewery  \n",
       "4                      Spa            Restaurant            Pizza Place  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add clustering labels\n",
    "neighborhoods_venues_sorted.insert(0, 'Cluster Labels', kmeans.labels_)\n",
    "\n",
    "toronto_merged = toronto_data2\n",
    "\n",
    "# merge toronto_grouped with toronto_data to add latitude/longitude for each neighborhood\n",
    "toronto_merged = toronto_merged.join(neighborhoods_venues_sorted.set_index('Neighborhood'), on='Neighborhood')\n",
    "\n",
    "# check the last columns!\n",
    "toronto_merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
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}
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                ).addTo(map_df167ff1f34a4d3ab0e2365befee6a2b);
            
    
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      ],
      "text/plain": [
       "<folium.folium.Map at 0x1a1c92c0b8>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visualize clusters\n",
    "# create map\n",
    "map_clusters = folium.Map(location=[latitude, longitude], zoom_start=11)\n",
    "\n",
    "# set color scheme for the clusters\n",
    "x = np.arange(kclusters)\n",
    "ys = [i + x + (i*x)**2 for i in range(kclusters)]\n",
    "colors_array = cm.rainbow(np.linspace(0, 1, len(ys)))\n",
    "rainbow = [colors.rgb2hex(i) for i in colors_array]\n",
    "\n",
    "# add markers to the map\n",
    "markers_colors = []\n",
    "for lat, lon, poi, cluster in zip(toronto_merged['Latitude'], toronto_merged['Longitude'], toronto_merged['Neighborhood'], toronto_merged['Cluster Labels']):\n",
    "    label = folium.Popup(str(poi) + ' Cluster ' + str(cluster), parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lon],\n",
    "        radius=5,\n",
    "        popup=label,\n",
    "        #color=rainbow[cluster-1],\n",
    "        fill=True,\n",
    "        #fill_color=rainbow[cluster-1],\n",
    "        fill_opacity=0.7).add_to(map_clusters)\n",
    "       \n",
    "map_clusters"
   ]
  }
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